fix lint

Author: merrymercy

tutorials/auto_scheduler/README.txt

@@ -1,2 +1,2 @@
 AutoScheduler : Template-free Auto Scheduling
------------
+---------------------------------------------

tutorials/auto_scheduler/tune_matmul_x86.py

@@ -78,7 +78,7 @@ def matmul_add(N, L, M, dtype):
 # * see :any:`auto_schedule.TuningOptions`: for more parameters
 
 tune_option = auto_scheduler.TuningOptions(
-    num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile('matmul.json')]
+    num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile("matmul.json")]
 )
 
 ######################################################################
@@ -97,7 +97,7 @@ def matmul_add(N, L, M, dtype):
 
 ######################################################################
 # Check correctness
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# ^^^^^^^^^^^^^^^^^
 # We build the binary and check its correctness
 
 func = tvm.build(sch, args)
@@ -115,7 +115,7 @@ def matmul_add(N, L, M, dtype):
 # Using the record file
 # ^^^^^^^^^^^^^^^^^^^^^
 # During the search, all measuremnt records is dumpped into the record
-# file "matmul.json". The measurement records can be used to resume the 
+# file "matmul.json". The measurement records can be used to resume the
 # search, re-apply search results and other analysis.
 #
 # Here we show an example where we load the best schedule from a file,
@@ -131,4 +131,3 @@ def matmul_add(N, L, M, dtype):
 # log file without reruning the search again.
 sch, args = task.compute_dag.apply_steps_from_state(inp.state)
 func = tvm.build(sch, args)
-

tutorials/autotvm/README.txt

@@ -1,4 +1,4 @@
 .. _tutorials-autotvm-sec:
 
 AutoTVM : Template-based Auto Tuning
------------
+------------------------------------